|
|
|
|
LEADER |
02954nam a22005655i 4500 |
001 |
978-3-319-46379-7 |
003 |
DE-He213 |
005 |
20160920153717.0 |
007 |
cr nn 008mamaa |
008 |
160920s2016 gw | s |||| 0|eng d |
020 |
|
|
|a 9783319463797
|9 978-3-319-46379-7
|
024 |
7 |
|
|a 10.1007/978-3-319-46379-7
|2 doi
|
040 |
|
|
|d GrThAP
|
050 |
|
4 |
|a Q334-342
|
050 |
|
4 |
|a TJ210.2-211.495
|
072 |
|
7 |
|a UYQ
|2 bicssc
|
072 |
|
7 |
|a TJFM1
|2 bicssc
|
072 |
|
7 |
|a COM004000
|2 bisacsh
|
082 |
0 |
4 |
|a 006.3
|2 23
|
245 |
1 |
0 |
|a Algorithmic Learning Theory
|h [electronic resource] :
|b 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings /
|c edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles.
|
264 |
|
1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
|
300 |
|
|
|a XIX, 371 p. 21 illus.
|b online resource.
|
336 |
|
|
|a text
|b txt
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
347 |
|
|
|a text file
|b PDF
|2 rda
|
490 |
1 |
|
|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 9925
|
505 |
0 |
|
|a Error bounds, sample compression schemes -- Statistical learning, theory, evolvability -- Exact and interactive learning -- Complexity of teaching models -- Inductive inference -- Online learning -- Bandits and reinforcement learning -- Clustering.
|
520 |
|
|
|a This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
|
650 |
|
0 |
|a Computer science.
|
650 |
|
0 |
|a Computers.
|
650 |
|
0 |
|a Data mining.
|
650 |
|
0 |
|a Artificial intelligence.
|
650 |
|
0 |
|a Pattern recognition.
|
650 |
1 |
4 |
|a Computer Science.
|
650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
|
650 |
2 |
4 |
|a Theory of Computation.
|
650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
|
650 |
2 |
4 |
|a Pattern Recognition.
|
700 |
1 |
|
|a Ortner, Ronald.
|e editor.
|
700 |
1 |
|
|a Simon, Hans Ulrich.
|e editor.
|
700 |
1 |
|
|a Zilles, Sandra.
|e editor.
|
710 |
2 |
|
|a SpringerLink (Online service)
|
773 |
0 |
|
|t Springer eBooks
|
776 |
0 |
8 |
|i Printed edition:
|z 9783319463780
|
830 |
|
0 |
|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 9925
|
856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-46379-7
|z Full Text via HEAL-Link
|
912 |
|
|
|a ZDB-2-SCS
|
912 |
|
|
|a ZDB-2-LNC
|
950 |
|
|
|a Computer Science (Springer-11645)
|